542 citations · 744 across the 16 of their papers we have counts for
17 papers
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
Huiwei Lin, Baoquan Zhang, Shanshan Feng +2
Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are…
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Baoquan Zhang, Hao Jiang, Xutao Li +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-le…
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
Baoquan Zhang, Shanshan Feng, Xutao Li +2
Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to addr…
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
Chuyao Luo, ZhengZhang, Rui Ye +2
Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasin…
Prototype Completion for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye +1
Few-shot learning aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tun…
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Baoquan Zhang, Xutao Li, Shanshan Feng +2
Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-train…
Prototype Completion with Primitive Knowledge for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye +2
Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the probl…
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
Yuan Yao, Xutao Li, Yu Zhang +1
Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing H…
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
Baoquan Zhang, Ka-Cheong Leung, Yunming Ye +1
Meta-learning has been proved to be an effective framework to address few-shot learning problems. The key challenge is how to minimize the generalization error of base learner acro…
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Feng Liu, Weiwen Liu, Xutao Li +1
Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling th…
Heterogeneous Domain Adaptation via Soft Transfer Network
Yuan Yao, Yu Zhang, Xutao Li +1
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a…
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Feng Liu, Ruiming Tang, Xutao Li +5
Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regressio…
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
Huifeng Guo, Ruiming Tang, Yunming Ye +2
The KNN approach, which is widely used in recommender systems because of its efficiency, robustness and interpretability, is proposed for session-based recommendation recently and…
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +3
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias…
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Feng Liu, Ruiming Tang, Xutao Li +3
Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delive…
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +2
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a str…
A Graph-based Push Service Platform
Huifeng Guo, Ruiming Tang, Yunming Ye +2
It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line syst…